In CARSWe Trust: How Context-Aware Recommendations Affect Customers’ Trust And Other Business Performance Measures Of Recommender Systems

Most of the work on Context-Aware Recommender Systems (CARSes) has focused on demonstrating that the contextual information leads to more accurate recommendations and on developing efficient recommendation algorithms utilizing this additional contextual information. Little work has been done, however, on studying how much the contextual information affects purchasing behavior and trust of customers. In this paper, we study how including context in recommendations affects customers’ trust, sales and other crucial business-related performance measures. To do this, we performed a live controlled experiment with real customers of a commercial European online publisher. We delivered content-based recommendations and context-aware recommendations to two groups of customers and to a control group. We measured the recommendations’ accuracy and diversification, how much customers spent purchasing products during the experiment, quantity and price of their purchases and the customers’ level of trust. We aim at demonstrating that accuracy and diversification have only limited direct effect on customers’ purchasing behavior, but they affect trust which drives the customer purchasing behavior. We also want to prove that CARSes can increase both recommendations’ accuracy and diversification compared to other recommendation engines. This means that including contextual information in recommendations not only increases accuracy, as was demonstrated in previous studies, but it is crucial for improving trust which, in turn, can affect other business-related performance measures, such as company’s sales.Polytechnic of Bari, Italy; NYU Stern School of Busines

Comparing Context-Aware Recommender Systems in Terms of Accuracy and Diversity: Which Contextual Modeling, Pre-filtering and Post-Filtering Methods Perform the Best

Although the area of Context-Aware Recommender Systems (CARS) has made a significant progress over the last several years, the problem of comparing various contextual pre-filtering, post-filtering and contextual modeling methods remained fairly unexplored. In this paper, we address this problem and compare several contextual pre-filtering, post-filtering and contextual modeling methods in terms of the accuracy and diversity of their recommendations to determine which methods outperform the others and under which circumstances. To this end, we consider three major factors affecting performance of CARS methods, such as the type of the recommendation task, context granularity and the type of the recommendation data. We show that none of the considered CARS methods uniformly dominates the others across all of these factors and other experimental settings; but that a certain group of contextual modeling methods constitutes a reliable “best bet” when choosing a sound CARS approach since they provide a good balance of accuracy and diversity of contextual recommendations.Politecnico di Bari, Italy; NYU Stern School of Busines

Archetypes of incumbents' strategic responses to digital innovation

Digital technologies (DTs) are significantly changing industrial and organisational activities, as well as the underlying processes and competencies. These impacts are particularly relevant when referring to firms’ business models, in particular on how incumbents have struggled to innovate their business model to react to the disruption triggered by DTs. These technologies have posed new challenges that seem to differ from those going along with previous technological shifts. We argue that such challenges depend on the incremental or radical nature of the technology at stake, as well as how far this is from the technological path of the incumbent, focal firm. By investigating how incumbents are adapting their business models in response to the disruption triggered by DTs, this paper proposes a conceptual matrix that draws on two dimensions: (i) the extent to which the impact of the digital technology is incremental or radical; and (ii) whether the industry of origin of the digital technology is the same or a different one from the focal firm. Through four illustrative case studies, we discuss different strategic approaches, highlighting how incumbents may mobilise different resources and assets following a more defensive or proactive posture in adapting their business model to the digital transformation

Incorporating Profit Margins into Recommender Systems: A Randomized Field Experiment of Purchasing Behavior and Consumer Trust

A number of recent studies have proposed new recommender designs that incorporate firm-centric measures (e.g., the profit margins of products) along with consumer-centric measures (e.g., relevance of recommended products). These designs seek to maximize the long-term profits from recommender deployment without compromising customer trust. However, very little is known about how consumers might respond to recommender algorithms that account for product profitability. We tested the impact of deploying a profit-based recommender on its precision and usage, as well as customer purchasing and trust, with data from an online randomized field experiment. We found that the profit-based algorithm, despite potential concerns about its negative impact on consumers, is effective in retaining consumers’ usage and purchase levels at the same rate as a content-based recommender. We also found that the profit-based algorithm generated higher profits for the firm. Further, to measure trust, we issued a post-experiment survey to participants in the experiment; we found there were no significant differences in trust across treatment. We related the survey results to the accuracy and diversity of recommendations and found that accuracy and diversity were both positively and significantly related to trust. The study has broader implications for firms using recommenders as a marketing tool, in that the approach successfully addresses the relevance-profit tradeoff in a real-world context

A framework towards resilient Mediterranean eco-solutions for small-scale farming systems

The impacts of climate change on crop and livestock sectors are well-documented. Climate change and its related events (e.g., high temperatures, extreme events, disease outbreaks) afect livestock production in various ways (e.g., nutrition, housing, health, welfare), and tend to compromise the physical productivity and the economic performances. Understanding animal responses to climate change may help planning strategies to cope with the adverse climatic conditions and also to reduce polluting emissions. Through an interdisciplinary approach, we develop a conceptual framework to assess and develop new organisational models for Mediterranean small-scale farming systems so as to mitigate the impacts of climate change, to improve farm management and farming tech‑ nologies, and to achieve an efective adaptation to the climate changes. The conceptual framework consists of four phases: (i) community engagement, (ii) strategies development, (iii) data collection and analysis, (iv) business model generation and sustainability assessment. We assess strengths, weaknesses, opportunities, and threats of the ecosolutions by mean of a SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis technique.info:eu-repo/semantics/publishedVersio

How to use recommender systems in e-business domains

Recommender systems (RS) were developed by research as a means to manage the information retrieval problem for users searching large databases. Recently they have become very popular among businesses as online marketing tools. Several online companies base their success on these systems, among other conditions. By looking at the last decades, the research on RS can be summarized into two main streams. The first research stream is focused on technical aspects of the algorithms and on identifying new ways to make them more accurate, while the second stream is focused on the effects of RS on customers. Therefore, we can draw several indications from the research on RS about the mistakes that companies should avoid when using RS. In this work we conduct an extensive literature and industrial review and we identify some crucial points managers should mind when developing a RS in order to make it as effective as possible in real world applications, or at least to avoid making it a failure

Does the Users' Tendency to Seek Information Affect Recommender Systems' Performance?

Much work has been done on developing recommender system (RS) algorithms, on comparing them using business metrics (such as customers' trust or perception of recommendations' novelty) and on exploring users' reactions to recommendations. It was demonstrated that different recommender systems perform differently on several performance metrics and that different users react differently to the same kind of recommendations. As a consequence, some scholars challenged to explore how users with different tendency to seek information during their purchasing process may react to different kind of recommendations. To the best of our knowledge, none of the prior works studied if users' tendency to seek information has an effect on recommender systems' performance. Different users may traditionally have different propensity to seek information and to receive suggestions and therefore they may react differently to the same recommendations. To this aim, we performed a live experiment with real customers coming from a European firm